Spectroscopic imaging combines digital imaging and optical spectroscopy techniques, which can include Raman scattering, fluorescence, photoluminescence, laser induced breakdown, ultraviolet, visible and infrared absorption spectroscopies. When applied to the chemical analysis of materials, spectroscopic imaging is also referred to as chemical imaging. Instruments for performing spectroscopic (i.e. chemical) imaging typically comprise an illumination source, image gathering optics, focal plane array imaging detectors and imaging spectrometers.
In general, the size or accessibility of a sample determines the choice of image gathering optic. For example, a microscope is typically employed for the analysis of sub-micron to millimeter spatial dimension samples. For larger objects, in the range of millimeter to meter dimensions, macro lens optics are appropriate. For samples located within relatively inaccessible environments, flexible fiberscope or rigid borescopes can be employed. For very large scale objects, such as planetary objects, or for objects located at a significant standoff distance from a sensor, telescopes are appropriate image gathering optics.
Two-dimensional, imaging focal plane array (FPA) detectors are typically employed to detect images formed by the various optical systems. The choice of FPA detector is governed by the spectroscopic technique employed to characterize the sample of interest. For example, silicon (Si) charge-coupled device (CCD) detectors or complementary metal-oxide-semiconductor (CMOS) detectors are typically employed with visible wavelength fluorescence and Raman spectroscopic imaging systems, while indium gallium arsenide (InGaAs) FPA detectors are typically employed with near infrared spectroscopic imaging systems.
Conventional spectroscopic devices operate over a limited range of wavelengths due to the operation ranges of the detectors or imaging spectrometers possible. This enables analysis in the ultraviolet (UV), visible (VIS), near infrared (NIR), short wave infrared (SWIR), mid infrared (MIR), and long wave infrared (LWIR) wavelengths, as well as some overlapping ranges. These correspond to wavelengths of about 180-380 nm (UV), about 380-700 nm (VIS), about 700-2500 nm (NIR), about 850-1700 nm (SWIR), about 700-1700 (VIS-NIR), about 2500-5000 nm (MIR), and about 5000-25000 (LWIR).
Spectroscopic imaging of a sample is commonly implemented by one of two methods. First, point-source illumination can be used on a sample to measure the spectra at each point of the illuminated area. Second, spectra can be collected over the entire area encompassing a sample simultaneously using an electronically tunable optical imaging filter such as an acousto-optic tunable filter (AOTF), a multi-conjugate tunable filter (MCF), or a liquid crystal tunable filter (LCTF). Here, the organic material in such optical filters is actively aligned by applied voltages to produce the desired bandpass and transmission function. The spectra obtained for each pixel of an image forms a complex data set referred to as a hyperspectral image. Hyperspectral images may contain the intensity values at numerous wavelengths or the wavelength dependence of each pixel element in the image. Multivariate routines, such as chemometric techniques, may be used to convert spectra to classifications.
Currently, tunable optical filter technology is limited to single bandpass, low throughput operation. Therefore, multiple, discrete bandpass measurements are required for analyte discrimination. The need for multiple measurements translates directly into overall measurement time.
Multivariate Optical Computing (MOC) is an approach which utilizes a compressive sensing device (e.g. an optical computer) to analyze spectroscopic data as it is collected. Other approaches utilize hard coated optical computing filters such as Multivariate Optical Elements (MOEs). MOEs are application-specific optical thin film filters that are used in transmission and reflectance modes. The radiometric response of a MOE-based instrument is proportional to the intended analyte in an associated matrix. While compressive sensing holds potential for decreasing measurement time, the use of MOEs have limitations. For example, MOEs are fixed and lack flexibility for adapting to different analytes.
There exists a need for an adaptable filter that can be used to detect a wide variety of analytes while reducing overall measurement time.